Existing computers based on the von Neumann architecture are usually more power-hungry and less effective than their biological counterparts—central nervous systems—for a wide range of tasks, such as perception, communication, learning, and decision making. With the increasing volume of data associated with big data processing, it becomes beneficial to develop computers that can learn, combine, and analyze vast amounts of information quickly and efficiently. For example, speech recognition software (e.g., Apple's Siri) is typically executed in the cloud because the involved computations are usually too challenging for hardware in a mobile phone.
One approach to address the shortcomings of von Neumann computing architectures is to develop artificial neural networks (ANNWs). ANNWs generally mimic the signal processing architecture in the brain and have recently received an explosion of interests. They can dramatically improve speech recognition, visual object recognition, object detection, and many other domains, such as drug discovery and genomics. Conventional artificial neural networks usually use electronic architectures, such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs). However, the computational speed and power efficiency achieved with these hardware architectures are still limited by electronic clock rates and ohmic losses.